Cardiovascular disease in kidney donors: matched cohort study
Bibliographic record
Abstract
OBJECTIVE: To determine whether people who donate a kidney have an increased risk of cardiovascular disease. DESIGN: Retrospective population based matched cohort study. PARTICIPANTS: All people who were carefully selected to become a living kidney donor in the province of Ontario, Canada, between 1992 and 2009. The information in donor charts was manually reviewed and linked to provincial healthcare databases. Matched non-donors were selected from the healthiest segment of the general population. A total of 2028 donors and 20,280 matched non-donors were followed for a median of 6.5 years (maximum 17.7 years). Median age was 43 at the time of donation (interquartile range 34-50) and 50 at the time of follow-up (42-58). MAIN OUTCOME MEASURES: The primary outcome was a composite of time to death or first major cardiovascular event. The secondary outcome was time to first major cardiovascular event censored for death. RESULTS: The risk of the primary outcome of death and major cardiovascular events was lower in donors than in non-donors (2.8 v 4.1 events per 1000 person years; hazard ratio 0.66, 95% confidence interval 0.48 to 0.90). The risk of major cardiovascular events censored for death was no different in donors than in non-donors (1.7 v 2.0 events per 1000 person years; 0.85, 0.57 to 1.27). Results were similar in all sensitivity analyses. Older age and lower income were associated with a higher risk of death and major cardiovascular events in both donors and non-donors when each group was analysed separately. CONCLUSIONS: The risk of major cardiovascular events in donors is no higher in the first decade after kidney donation compared with a similarly healthy segment of the general population. While we will continue to follow people in this study, these interim results add to the evidence base supporting the safety of the practice among carefully selected donors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".